Analyses of Crash Occurrence and Injury Severities on Multi Lane Highways using Machine Learning Algorithms
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @PhdThesis{Das:thesis,
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author = "Abhishek Das",
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title = "Analyses of Crash Occurrence and Injury Severities on
Multi Lane Highways using Machine Learning Algorithms",
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school = "Department of Civil, Environmental, and Construction
Engineering (CECE) of the University of Central
Florida",
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year = "2009",
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address = "Orlando, USA",
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month = "13 " # oct,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://www.cecs.ucf.edu/graddefense/pdf/10",
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URL = "http://purl.fcla.edu/fcla/etd/CFE0002928",
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size = "212 pages",
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abstract = "Reduction of crash occurrence on the various roadway
locations (mid-block segments; signalized
intersections; un-signalized intersections) and the
mitigation of injury severity in the event of a crash
are the major concerns of transportation safety
engineers. Multi lane arterial roadways (excluding
freeways and expressways) account for forty-three
percent of fatal crashes in the state of Florida.
Significant contributing causes fall under the broad
categories of aggressive driver behavior; unforgiving
weather and environmental conditions; and roadway
geometric and traffic factors. The objective of this
research was the implementation of innovative,
state-of-the-art analytical methods to identify the
contributory factors for crashes and injury severity.
Advances in computational methods render the use of
modern statistical and machine learning algorithms.
Even though most of the contributing factors are known
a-priori, advanced methods unearth changing trends.
Heuristic evolutionary processes such as linear genetic
programming; sophisticated data mining methods like
conditional inference tree; and mathematical treatments
in the form of sensitivity analyses outline the major
contributions in this research. Application of
traditional statistical methods like simultaneous
ordered probit models, identification and resolution of
crash data problems are also key aspects of this study.
In order to eliminate the use of unrealistic uniform
intersection influence radius of 250 ft, heuristic
rules were developed for assigning crashes to roadway
segments, junctions with traffic lights intersection
and access points using parameters, such as 'site
location' and 'traffic control'. Use of Conditional
Inference Forest instead of Classification and
Regression Tree to identify variables of significance
for injury severity analysis removed the bias towards
the selection of continuous variable or variables with
large number of categories. Concepts of evolutionary
biology like crossover and mutation were implemented to
develop models for classification and regression
analyses based on the highest hit rate and minimum
error rate, respectively. Annual daily traffic;
friction coefficient of pavements; on-street parking;
curbed medians; surface and shoulder widths; alcohol /
drug usage are some of the significant factors that
played a role in both the crash occurrence and injury
severities. Relative sensitivity analyses were used to
identify the effect of continuous variables on the
variation of crash counts. This study improved the
understanding of the significant factors that could
play an important role in designing better safety
countermeasures on multi lane highways, and hence
enhance their safety by reducing the frequency of
crashes and severity of injuries.",
-
notes = "Supervisor Mohamed A. Abdel-Aty",
- }
Genetic Programming entries for
Abhishek Das
Citations